Latent Class Analysis of Depression-Fatigue-Pain Interference Symptoms and Associated Demographic Factors by sex in Chinese patients with stroke: A Cross-Sectional Study.

Abstract

Purpose: Patients with stroke often experience a series of symptoms during treatment and rehabilitation, which may present various characteristics in different subgroups. The aims of this study were to explore the characteristics of latent class groups of depression, fatigue, and pain in patients of different sexes with stroke and to determine the influence of demographic characteristics on different latent class groups by sex.

Methods: The data of 501 patients with stroke were collected from two tertiary hospitals using convenience sampling between March 2022 and September 2022. The three-domain short forms of PROMIS were measured. Two homogenous classes were identified in the men and women groups using the latent class analysis (LCA) method. Multivariable logistic regression analyses were used to examine the relationships of latent classes with demographic data by sex.

Results: For the 501 patients studied, the LCA model fit with the two latent classes was statistically significant for both men and women. In the men group, Class 1 comprised 38.8% of the men population, Class 2 made up the remaining 61.2%, and the probability of membership was 52.2% and 47.8% for Class 1 and Class 2 in the women, respectively. Women had more severe symptom characteristics and more demographically impacted parameters than men. The factors that influenced male and female patients differed, with household monthly income having the same influence in both groups.

Conclusion: This study found that the latent classes of patients with stroke were highly heterogeneous, with women having more severe symptom characteristics and demographic differences.